SoroushVahidi/scidocs huggingface dataset:
A search engine for 3D models
(2003)

As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this article, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a Web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For the shape-based queries, we have developed a new matching algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46 to 245% better performance than related shape-matching methods during precision--recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under a second. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models).
Robotic excavation in construction automation
(2002)

C onstruction is of prime economic significance to many industry sectors. Intense competition, shortages of skilled labor, and technological advances are forcing rapid change in the construction industry, thus motivating construction automation [1]. Earthmoving machines, such as bulldozers, wheel loaders, excavators, scrapers, and graders, are common in construction. Excavationbased operations are used in general earthmoving, digging, and sheet-piling for displacing large amounts of material. On a smaller scale, operations such as trenching and footing formation require precisely controlled excavation. Although the fully automated construction site is still a dream of some civil engineers, research developments have shown the promise of robotics and automation in construction [2]. Despite the apparent economic importance of excavation in construction, there have been few implementations of autonomous or teleoperated excavators. A number of researchers have investigated the feasibility of automating excavation. Many of these studies have addressed the possible use of autonomous excavators during unmanned phases of establishing manned Lunar or Martian research stations [3], [4]. Much of the work on terrestrial excavation has focused on teleoperation, rather than on the system requirements for autonomous operation. Although there have been a number of valuable theoretical and experimental contributions to the field of autonomous, robotic or teleoperated excavation [5]-[8], autonomous operation of a full-scale excavator has not been commercially demonstrated. Many of the experimental studies reported in the literature involve using conventional industrial robots fitted with buckets to excavate in a bed of loose sand. While there are parallels between “classical” robotics and robotic excavation, there are also some pronounced differences. In particular, an excavator is not fixed relative to the work piece; it plastically deforms the work piece by applying large forces and is caused to move relative to the soil by the same large forces. Furthermore, strategic and bucket trajectory planning must necessarily occur in a dynamic environment; if the excavator is not changing the profile of the soil being worked, it is not doing useful work. This article presents some results of the autonomous excavation project conducted at the Australian Centre for Field Robotics (ACFR) with a focus on construction automation. The application of robotic technology and computer control is one key to construction industry automation. Excavation automation is a multidisciplinary task, encompassing a broad area of research and development planning monitoring environment sensing and modeling navigation machine modeling and control. The ultimate goal of the ACFR excavation project is to demonstrate fully autonomous execution of excavation tasks in
Convolutional CRFs for Semantic Segmentation
(2018)

For the challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In more recent works however, CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow training and inference speeds of CRFs, as well as the difficulty of learning the internal CRF parameters. To overcome both issues we propose to add the assumption of conditional independence to the framework of fully-connected CRFs. This allows us to reformulate the inference in terms of convolutions, which can be implemented highly efficiently on GPUs. Doing so speeds up inference and training by two orders of magnitude. All parameters of the convolutional CRFs can easily be optimized using backpropagation. To facilitating further CRF research we make our implementation publicly available. Please visit: https://github.com/MarvinTeichmann/ConvCRF
CBR: Strengths and Weaknesses
(1998)

There is considerable enthusiasm about Case-Based Reasoning as a means of developing knowledge-based systems. There are two broad reasons for this enthusiasm. First, it is evident that much of human expert competence is experience based and it makes sense to adopt a reuse-based methodology for developing knowledge based systems. The other reason is the expectation that using Case-Based Reasoning to develop knowledge based systems will i nvolve less knowledge engineering than alternative ‘ first-principles’ based approaches. In this paper I explore the veracity of this assertion and outline the types of situation in which it will be true. CBR is perceived to have this knowledge engineering advantage because it allows the development of knowledge based systems in weak theory domains. If CBR can work without formalising a domain theory then there is a question about the quality of solutions produced by case-based systems. This is the other issue discussed in this paper and situations where CBR will and will not produce good quality solutions are outlined.
Modeling of braided pneumatic actuators for robotic control
(2001)

Braided Pneumatic Actuators exhibit non-linear forcelength properties grossly similar to muscle, and have a high strength-to-weight ratio. These properties make them desirable for legged robots. This work emphasizes understanding the actuator properties for later use in simulation and control of legged robots. Static and dynamic mathematical models for the actuators are reported and verified through testing and simulation. In addition, a dynamic air flow model modulated by a solenoid valve was developed to provide modular actuation subroutines for simulation.
Review of performance metrics for green data centers: a taxonomy study
(2011)

Data centers now play an important role in modern IT infrastructures. Although much research effort has been made in the field of green data center computing, performance metrics for green data centers have been left ignored. This paper is devoted to categorization of green computing performance metrics in data centers, such as basic metrics like power metrics, thermal metrics and extended performance metrics i.e. multiple data center indicators. Based on a taxonomy of performance metrics, this paper summarizes features of currently available metrics and presents insights for the study on green data center computing.
Camera Augmented Mobile C-Arm (CAMC): Calibration, Accuracy Study, and Clinical Applications
(2010)

Mobile C-arm is an essential tool in everyday trauma and orthopedics surgery. Minimally invasive solutions, based on X-ray imaging and coregistered external navigation created a lot of interest within the surgical community and started to replace the traditional open surgery for many procedures. These solutions usually increase the accuracy and reduce the trauma. In general, they introduce new hardware into the OR and add the line of sight constraints imposed by optical tracking systems. They thus impose radical changes to the surgical setup and overall procedure. We augment a commonly used mobile C-arm with a standard video camera and a double mirror system allowing real-time fusion of optical and X-ray images. The video camera is mounted such that its optical center virtually coincides with the C-arm's X-ray source. After a one-time calibration routine, the acquired X-ray and optical images are coregistered. This paper describes the design of such a system, quantifies its technical accuracy, and provides a qualitative proof of its efficiency through cadaver studies conducted by trauma surgeons. In particular, it studies the relevance of this system for surgical navigation within pedicle screw placement, vertebroplasty, and intramedullary nail locking procedures. The image overlay provides an intuitive interface for surgical guidance with an accuracy of <;1 mm, ideally with the use of only one single X-ray image. The new system is smoothly integrated into the clinical application with no additional hardware especially for down-the-beam instrument guidance based on the anteroposterior oblique view, where the instrument axis is aligned with the X-ray source. Throughout all experiments, the camera augmented mobile C-arm system proved to be an intuitive and robust guidance solution for selected clinical routines.
Semi-Supervised Classification with Graph Convolutional Networks
(2016)

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.
ArnetMiner: extraction and mining of academic social networks
(2008)

This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.
SimRank: a measure of structural-context similarity
(2002)

The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.
Inductive Conformal Prediction: Theory and Application to Neural Networks
(2008)

Traditional machine learning algorithms for pattern recognition just output simple predictions, without any associated confidence values. Confidence values are an indication of how likely each prediction is of being correct. In the ideal case, a confidence of 99% or higher for all examples in a set, means that the percentage of erroneous predictions in that set will not exceed 1%. Knowing the likelihood of each prediction enables us to assess the extent to which we can rely on it. For this reason, predictions that are associated with some kind of confidence values are highly desirable in many risk-sensitive applications, such as those used for medical diagnosis or financial analysis. In fact, such information can benefit any application that requires human-computer interaction, as confidence values can be used to determine the way in which each prediction will be treated. For instance, a filtering mechanism can be employed so that only predictions which satisfy a certain level of confidence will be taken into account, while the rest can be discarded or passed on to a human for judgement. There are two main areas in mainstream machine learning that can be used in order to obtain some kind of confidence values; the Bayesian framework and the theory of Probably Approximately Correct learning (PAC theory). Quite often the Bayesian framework is used for producing algorithms that complement individual predictions with probabilistic measures of their quality. On the other hand, PAC theory can be used for producing upper bounds on the probability of error for a given algorithm with respect to some confidence level 1 − δ. Both of these approaches however, have their drawbacks. In order to apply the Bayesian framework one is required to have some prior knowledge about the distribution that generates the data. When the correct prior is known, Bayesian methods provide optimal decisions. For real world data sets though, as the required knowledge is not available, one has to assume the existence of an arbitrarily chosen prior. In this case, if the assumed prior is incorrect, the resulting confidence levels may also be “incorrect”; for example the predictive regions output for the 95% confidence level may contain the true label in much less than 95% of the cases. This signifies a major failure, as we would expect confidence levels to bound the percentage of expected errors. An experimental demonstration of how misleading Bayesian methods can become when their assumptions are violated can be found in (Melluish et al., 2001).
An unsupervised long short-term memory neural network for event detection in cell videos
(2017)

We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events in cell video sequences. Cells in images that are captured from various biomedical applications usually have different shapes and motility, which pose difficulties for the automated event detection in cell videos. Current methods to detect cellular events are based on supervised machine learning and rely on tedious manual annotation from investigators with specific expertise. So that our LSTM network could be trained in an unsupervised manner, we designed it with a branched structure where one branch learns the frequent, regular appearance and movements of objects and the second learns the stochastic events, which occur rarely and without warning in a cell video sequence. We tested our network on a publicly available dataset of densely packed stem cell phase-contrast microscopy images undergoing cell division. This dataset is considered to be more challenging that a dataset with sparse cells. We compared our method to several published supervised methods evaluated on the same dataset and to a supervised LSTM method with a similar design and configuration to our unsupervised method. We used an F1-score, which is a balanced measure for both precision and recall. Our results show that our unsupervised method has a higher or similar F1-score when compared to two fully supervised methods that are based on Hidden Conditional Random Fields (HCRF), and has comparable accuracy with the current best supervised HCRF-based method. Our method was generalizable as after being trained on one video it could be applied to videos where the cells were in different conditions. The accuracy of our unsupervised method approached that of its supervised counterpart.
Ensuring Data Integrity in Cloud Computing
(2012)

Cloud computing provides convenient on-demand network access to a shared pool of configurable computing resources. The resources can be rapidly deployed with great efficiency and minimal management overhead. Cloud is an insecure computing platform from the view point of the cloud users, the system must design mechanisms that not only protect sensitive information by enabling computations with encrypted data, but also protect users from malicious behaviours by enabling the validation of the computation result. In this paper, we propose a new data encoding scheme called layered interleaving, designed for time-sensitive packet recovery in the presence of bursty loss. It is high-speed data recovery scheme with minimal loss probability and using a forward error correction scheme to handle bursty loss. The proposed approach is highly efficient in recovering the singleton losses almost immediately and from bursty data losses.
Self-organized structures in a superorganism : do ants “ behave ” like molecules ?
(2006)

While the striking structures (e.g. nest architecture, trail networks) of insect societies may seem familiar to many of us, the understanding of pattern formation still constitutes a challenging problem. Over the last two decades, self-organization has dramatically changed our view on how collective decision-making and structures may emerge out of a population of ant workers having each their own individuality as well as a limited access to information. A variety of collective behaviour spontaneously outcome from multiple interactions between nestmates, even when there is no directing influence imposed by an external template, a pacemaker or a leader. By focussing this review on foraging structures, we show that ant societies display some properties which are usually considered in physico-chemical systems, as typical signatures of self-organization. We detail the key role played by feed-back loops, fluctuations, number of interacting units and sensitivity to environmental factors in the emergence of a structured collective behaviour. Nonetheless, going beyond simple analogies with non-living self-organized patterns, we stress on the specificities of social structures made of complex living units of which the biological features have been selected throughout the evolution depending on their adaptive value. In particular, we consider the ability of each ant individual to process information about environmental and social parameters, to accordingly tune its interactions with nestmates and ultimately to determine the final pattern emerging at the collective level. We emphasize on the parsimony and simplicity of behavioural rules at the individual level which allow an efficient processing of information, energy and matter within the whole colony. © 2006 Elsevier B.V. All rights reserved. PACS: 87.18.Bb; 87.23.Cc; 87.23.Ge; 89.75.-k; 89.75.Fb; 89.75.Kd
One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations
(2015)

Discourse relations bind smaller linguistic units into coherent texts. Automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked arguments. A more subtle challenge is that it is not enough to represent the meaning of each argument of a discourse relation, because the relation may depend on links between lowerlevel components, such as entity mentions. Our solution computes distributed meaning representations for each discourse argument by composition up the syntactic parse tree. We also perform a downward compositional pass to capture the meaning of coreferent entity mentions. Implicit discourse relations are then predicted from these two representations, obtaining substantial improvements on the Penn Discourse Treebank.
Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour
(2014)

analysis of blood samples for counting leukemia cells is discussed in this paper. support vector machine and nearest neighbour concept is presented in this paper. The identification of blood disorders, it can lead to classification of certain diseases related to blood. Several classification techniques are investigated till today. One of the best methods for classification techniques nearest neighbour and SVM (Support Vector Machine).By identifying and counting blood cell within the blood smear using classification techniques it’s quite possible to detect so many diseases. If one of the new classifier is used i.e. nearest neighbour and SVM it is quiet possible to detect the cancer cell from the blood cell counting. Keyword: Blood, Classification technique, Nearest Neighbour Network, SVM
MOSFET switched 20 kV, 500 A, 100 ns pulse generator with series connected pulse transformers
(2001)

This paper describes a MOSFET switched pulser, which is composed of 30 series connections of pulse generating modules (PGMs) that can produce a 700 V/500 A/100 ns pulse with the rising time of 60 ns or less. All PGMs are connected in series through the specially designed pulse transformers to obtain 20 kV/500 A/100 ns pulses at repetition rates up to 20 kHz. To obtain the fast rising time pulse, it is essential to minimize the leakage inductance of the pulse transformer and the wiring inductance between pulse transformers. In this paper, two kinds of layout for the series connection of pulse transformers are introduced and compared to the conventional methods. Also, the authors minimized the losses by adopting an energy recovery circuit that returns to the source the stored energy in the clamp circuit of the primary windings of the pulse transformers. Experimental results are given for the proposed pulser under the resistor load and the wire-cylinder plasma reactor load, respectively.
Provisioning Software-Defined IoT Cloud Systems
(2014)

Cloud computing is ever stronger converging with the Internet of Things (IoT) offering novel techniques for IoT infrastructure virtualization and its management on the cloud. However, system designers and operations managers face numerous challenges to realize IoT cloud systems in practice, mainly due to the complexity involved with provisioning large-scale IoT cloud systems and diversity of their requirements in terms of IoT resources consumption, customization of IoT capabilities and runtime governance. In this paper, we introduce the concept of software-defined IoT units - a novel approach to IoT cloud computing that encapsulates fine-grained IoT resources and IoT capabilities in well-defined APIs in order to provide a unified view on accessing, configuring and operating IoT cloud systems. Our software-defined IoT units are the fundamental building blocks of software-defined IoT cloud systems. We present our framework for dynamic, on-demand provisioning and deploying such software-defined IoT cloud systems. By automating provisioning processes and supporting managed configuration models, our framework simplifies provisioning and enables flexible runtime customizations of software-defined IoT cloud systems. We demonstrate its advantages on a real-world IoT cloud system for managing electric fleet vehicles.